coreweave-install-auth
Configure CoreWeave Kubernetes Service (CKS) access with kubeconfig and API tokens. Use when setting up kubectl access to CoreWeave, configuring CKS clusters, or authenticating with CoreWeave cloud services. Trigger with phrases like "install coreweave", "setup coreweave", "coreweave kubeconfig", "coreweave auth", "connect to coreweave".
Best use case
coreweave-install-auth is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Configure CoreWeave Kubernetes Service (CKS) access with kubeconfig and API tokens. Use when setting up kubectl access to CoreWeave, configuring CKS clusters, or authenticating with CoreWeave cloud services. Trigger with phrases like "install coreweave", "setup coreweave", "coreweave kubeconfig", "coreweave auth", "connect to coreweave".
Teams using coreweave-install-auth should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/coreweave-install-auth/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How coreweave-install-auth Compares
| Feature / Agent | coreweave-install-auth | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Configure CoreWeave Kubernetes Service (CKS) access with kubeconfig and API tokens. Use when setting up kubectl access to CoreWeave, configuring CKS clusters, or authenticating with CoreWeave cloud services. Trigger with phrases like "install coreweave", "setup coreweave", "coreweave kubeconfig", "coreweave auth", "connect to coreweave".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# CoreWeave Install & Auth
## Overview
Set up access to CoreWeave Kubernetes Service (CKS). CKS runs bare-metal Kubernetes with NVIDIA GPUs -- no hypervisor overhead. Access is via standard kubeconfig with CoreWeave-issued credentials.
## Prerequisites
- CoreWeave account at https://cloud.coreweave.com
- `kubectl` v1.28+ installed
- Kubernetes namespace provisioned by CoreWeave
## Instructions
### Step 1: Download Kubeconfig
1. Log in to https://cloud.coreweave.com
2. Navigate to **API Access** > **Kubeconfig**
3. Download the kubeconfig file
```bash
# Save kubeconfig
mkdir -p ~/.kube
cp ~/Downloads/coreweave-kubeconfig.yaml ~/.kube/coreweave
# Set as active context
export KUBECONFIG=~/.kube/coreweave
# Verify connection
kubectl get nodes
kubectl get namespaces
```
### Step 2: Configure API Token
```bash
# CoreWeave API token for programmatic access
export COREWEAVE_API_TOKEN="your-api-token"
# Store securely
echo "COREWEAVE_API_TOKEN=${COREWEAVE_API_TOKEN}" >> .env
echo "KUBECONFIG=~/.kube/coreweave" >> .env
```
### Step 3: Verify GPU Access
```bash
# List available GPU nodes
kubectl get nodes -l gpu.nvidia.com/class -o custom-columns=\
NAME:.metadata.name,GPU:.metadata.labels.gpu\.nvidia\.com/class,\
STATUS:.status.conditions[-1].type
# Check GPU allocatable resources
kubectl describe nodes | grep -A5 "Allocatable:" | grep nvidia
```
### Step 4: Test with a Simple GPU Pod
```yaml
# test-gpu.yaml
apiVersion: v1
kind: Pod
metadata:
name: gpu-test
spec:
restartPolicy: Never
containers:
- name: cuda-test
image: nvidia/cuda:12.2.0-base-ubuntu22.04
command: ["nvidia-smi"]
resources:
limits:
nvidia.com/gpu: 1
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values: ["A100_PCIE_80GB"]
```
```bash
kubectl apply -f test-gpu.yaml
kubectl logs gpu-test # Should show nvidia-smi output
kubectl delete pod gpu-test
```
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| `Unable to connect to the server` | Wrong kubeconfig | Verify KUBECONFIG path |
| `Forbidden` | Missing namespace permissions | Contact CoreWeave support |
| No GPU nodes found | Wrong node labels | Check `gpu.nvidia.com/class` labels |
| Pod stuck Pending | GPU capacity exhausted | Try different GPU type or region |
## Resources
- [CoreWeave Documentation](https://docs.coreweave.com)
- [CKS Introduction](https://docs.coreweave.com/docs/products/cks)
- [GPU Instance Types](https://docs.coreweave.com/docs/platform/instances/gpu-instances)
## Next Steps
Proceed to `coreweave-hello-world` to deploy your first inference service.Related Skills
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